What courses do you need to take that are relevant to Data Science at LSE?

It depends very much on your course. In particular, if you are considering a masters, it would be definitely helpful to have a foundation in university level mathematics (Calculus and Linear Algebra) and statistics.

  • pre-requisite knowledge: for example Linear Algebra and Multivariate Calculus (in MA100, MA212), Statistics (ST100, ST202); they could provide the background for study of more advanced topics
  • more 'advanced topics': for example, Machine Learning (ST310) and Time Series Forecasting (ST304),
  • relevant domain-specific (economics, finance, social science) courses, such as: Principles of Econometrics (MA212) and Risk Management and Modelling (FM321); courses that also have a overlap

Feel free to submit a pull request if you would like to contribute!

Undergraduate Level

Department of Statistics

ST101 - Programming for Data Science (Half Unit)

  • Course Content : "The primary focus of the course is to cover principles of computer programming with a focus on data science applications."
  • Why you should take it: Chance to learn programming (Python) and concepts in a formal, academic setting. Taught by Chair and programme director of Data Science, Professor Milan Vojnovic
  • Prerequisites: None
  • Assessment: Coursework (40%) and project (60%) in the LT
  • Course Page

ST207 - Databases (Half Unit) | Not available in 2020 - 2021

  • Course Content: “The goal of this course is to cover basic concepts of database management systems, iuncluding relational and other types of database management systems."
  • Prerequisites: ST101 Programming for Data Science or ST115 Managing and Visualising Data
  • Why you should take it: Chance to learn about Structured Query Language(SQL) and Python, and to see how various theoretical principles are implemented in practice in a variety of database managemenet systems.Taught by Chair and programme director of Data Science, Professor Milan Vojnovic.
  • Assessment: Coursework (40%) and project (60%) in the LT.
  • Course Page

ST211 - Applied Regression (Half Unit)

  • Course Content: "Statistical data analysis in R covering: Simple and multiple linear regression, Model diagnostics, Detection of outliers, Multicollinearity,Introduction to GLMs."
  • Prerequisites: ST102 Elementary Statistical Theory
  • Why you should take it: High first rate(35.7% 2016/17-2018/19 combined), learn how to do regression in R.
  • Assessment: Exam(50%) in ST, Project(45%)in ST and Project(5%) in LT
  • Course Page

ST304 - Time Series and Forecasting (Half Unit)

  • Course Content: "The course introduces the student to the statistical analysis of time series data and simple models. What time series analysis can be useful for; autocorrelation; stationarity, trend removal and seasonal adjustment, basic time series models; AR, MA, ARMA; invertibility; spectral analysis; estimation; forecasting; introduction to financial time series and the GARCH models; unit root processes."
  • Prerequisites: 2nd year Statistics and Probability
  • Why you should take it: Time series methods have a wide range of applications to different fields
  • Assessment: Exam(100%) in ST
  • Course Page

ST308 - Bayesian Inference (Half Unit)

  • Course Content: Statistical decision theory, bayesian inference, implementation, applications
  • Prerequisites: MA100 Mathematical Methods, ST102 Elementary Statistical Theory
  • Why you should take it:
  • Assessment: Exam (80%) and Project(20%) in ST
  • Course Page
  • Github

ST309 - Elementary Data Analytics (Half Unit)

  • Course Content: "The primary focus of this course is to help students view various problems from business, economy/finance, and social domains from a data perspectives and understand the principles of extracting useful information and knowledge from data."
  • Prerequisites:a statistical course at least equivalent to Quantitative Methods (Statistics) (ST107) or Statistical Methods for the Social Sciences (ST108).
  • Why you should take it: "exceptionally good educational materials and teachers";taught by Prof Qiwei Yao.
  • Assessment: Coursework (30%) in MT and Project(70%) in LT
  • Course Page

ST310 - Machine Learning

  • Course Content: "The primary focus of this course is on the core machine learning techniques in the context of high-dimensional or large datasets (i.e. big data)."
  • Prerequisites:ST102 Elementary Statistical Theory; Familiarity with basic computer programming in R or Python.
  • Why you should take it: Chance to learn machine learning in a formal, academic setting, gain hands-on experience.
  • Assessment: Exam (70%) in the ST and Project (30%) in the MT Week 11.
  • Course Page

ST312 - Applied Statistics Project

  • Course Content: "Students will produce a project involving a critical investigation and collation of statistical data on a topic of their own interest."
  • Prerequisites:ST102 Elementary Statistical Theory
  • Why you should take it: Chance to improve your communication skill and you can work on a topic of your own interest using real-world data.
  • Assessment:Project (90%) and presentation (10%) in the ST.
  • Course Page
  • Moodle

Department of Mathematics

Mathematics Optional Courses films (Moodle): https://moodle.lse.ac.uk/course/view.php?id=2863#section-3

MA314 - Algorithms and Programming

MA316 - Graph Theory (Half Unit) | Not available 2020-2021

MA334 - Dissertation in Mathematics

MA320 - Mathematics of Networks

EC221 - Principles of Econometrics

EC309 - Econometric Theory

EC333 - Problems of Applied Econometrics

FM320 - Quantitative Finance | FM321* Risk Management and Modelling

  • Course Content: Statistical and Time Series methods (GARCH) as it relates to finance, mainly the forecasting of volatility and risk modelling.
  • Course Page

Masters Level

ST445 - Managing and Visualising Data (Half Unit)

  • Course Content: "The focus of the course is on the fundamental principles and best practices for data manipulation and visualisation. The course is based on using Python as the primary programming language and various software packages."
  • Assessment:Project (60%) and continuous assessment (40%) in the MT.
  • Course Page
  • Github Pages

ST446 - Distributed Computing For Big Data (Half Unit)

  • Course Content: "The course covers basic principles of systems for distributed processing of big data including distributed file systems; distributed computation models such as Mapreduce, resilient distributed datasets, and distributed dataflow graph computations; structured querying over large datasets; graph data processing systems; stream data processing systems; scalable machine learning algorithms for classification, regression, collaborative filtering, topic modelling and other tasks."
  • Prerequisites:Basic knowledge of Python or some other programming knowledge is desirable
  • Why you should take it: "The course enables students to learn about the principles and gain hands-on experience in working with the state of the art computing technologies";Taught by Chair and programme director of Data Science, Professor Milan Vojnovic.
  • Assessment:Project (80%) in the LT.Continuous assessment (10%) in the LT Week 4.Continuous assessment (10%) in the LT Week 7.
  • Course Page
  • Moodle
  • Github Pages

ST451 - Bayesian Machine Learning (Half Unit)

  • Course Content: "The course sets up the foundations and covers the basic algorithms covered in probabilistic machine learning. Several techniques that are probabilistic in nature are introduced and standard topics are revisited from a Bayesian viewpoint."
  • Prerequisites:Basic knowledge of Python or some other programming knowledge is desirable
  • Assessment: Exam (50%) and project (50%) in the ST.
  • Github)
  • Page

MY459 - Special Topics in Quantitative Analysis: Quantitative Text Analysis

MY470 - Computer Programming

MY472 - Data for Data Scientists

20/06/2020